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Reference evapotranspiration (ETo ) is an essential parameter of the hydrologic cycle that plays a key role in the water resources management, especially in arid and semi-arid regions where the tot...

The radiation-based Priestley–Taylor (PT) model is a common way of modeling evapotranspiration (ET0) when only air temperature and solar radiation records are obtainable. However, this method presents the crucial drawback of requiring an initial local calibration. Most of the existing calibration procedures usually utilize similar patterns (data) for calibrating and testing the ET0 models. In the current research, the PT model and its calibrated versions were evaluated by utilizing meteorological data from 29 weather stations in Iran. Thoroughgoing local and external data scrutinizing procedures (using k-fold validation) were adopted to evaluate the models. Additionally, the externally calibrated models were reevaluated considering vapor pressure deficit (VPD) and net radiation (Rn) records. A similar plan was replicated for assessing the performance of gene expression programming (GEP)–based equations fed with the air temperature and solar radiation data. Both local and external calibrations of the PT model generally improved the accuracy of original PT for estimating ET0, especially in humid and arid stations. External calibration using VPD and Rn data was found to be comparable to locally calibrated and non-calibrated PT models. Local GEP models generally performed more accurately than locally calibrated and non-calibrated PT models. Higher accuracy was also obtained using external GEP models in comparison with non-calibrated PT models.

Despite increasing use of pressurized irrigation methods, most irrigation projects worldwide still involve surface systems. Accurate estimation of the amount of infiltrating water and its spatial distribution in the soil is of great importance in the design and management of furrow irrigation systems. Moment analysis has previously been applied to describe the subsurface water distribution using input data from numerical simulations rather than field measured data, and assuming a constant ponding depth in the furrow. A field experiment was conducted in a blocked-end level furrow at Maricopa Agricultural Center, Arizona, USA, to study the effect of time-variable ponding depths on soil water distribution and the resulting wetting bulb under real conditions in the field using moment analysis. The simulated volumetric soil water contents run with variable and constant (average) ponding depths using HYDRUS 2D/3D were almost identical, and both compared favorably with the field data. Hence, only the simulated soil water contents with variable ponding depths were used to calculate the moments. It was concluded that the fluctuating flow depth had no significant influence on the resulting time-evolving ellipses. This was related to the negligible 10-cm variation in ponding depths compared to the high negative matric potential of the unsaturated soil.

Developing a reliable groundwater vulnerability and contamination risk map is very important for groundwater management and protection. This study aims to compare various modified DRASTIC vulnerability frameworks based on rate calibration using the Wilcoxon rank-sum test (WRST), frequency ratio (FR) and weight optimization using the correlation coefficient (CC), the analytic hierarchy process (AHP), and genetic algorithms (GA), as well as to introduce, for the first time, an aggregated approach based on a bagging ensemble to develop a combined modified DRASTIC model. This research was conducted in the Khoy plain, NW Iran. To develop a typical DRASTIC map, seven DRASTIC data layers were generated, weighted, and then overlaid in ArcGIS. The nitrate (NO3) concentrations at 54 sites in the study area were used to validate the models by calculating the correlation coefficient (r) between the vulnerability/risk indices and NO3 concentrations. The calculated r value for the typical DRASTIC was 0.12. A sensitivity analysis reveals that the impact of the vadose zone and conductivity parameters with mean variation indices of 22.2 and 7.5%, respectively, have the highest and lowest influence on aquifer vulnerability. The r values increased for all the optimized frameworks. The results show that the WRST and GA methods are the most effective methods for calibration and optimization of DRASTIC rates and weights, with the WRST-GA-DRASTIC model obtaining an r value of 0.64. A bagging ensemble model was employed to combine the advantages of each standalone model. The bagging ensemble model yields an r value of 0.67. The ensemble model has the potential to increase the r value further than both the standalone optimized frameworks and the typical DRASTIC approach. In terms of spatial distribution class area (%), the bagging ensemble-DRASTIC model demonstrates that the moderate and low contamination risk classes with 16.4 and 23.1% of the total area cover the lowest and highest parts of the plain.

Soil phosphorus (P) is a vital but limited element which is usually leached from the soil via the drainage process. Soil phosphorus as a soluble substance can be delivered through agricultural fields by runoff or soil loss. It is one of the most essential nutrients that affect the sustainability of crops as well as the energy transfer for living organisms. Therefore, an accurate simulation of soil phosphorus, which is considered as a point source pollutant in elevated contents, must be performed. Considering a crucial issue for a sustainable soil and water management, an effective soil phosphorus assessment in the current research was conducted with the aim of examining the capability of five different wavelet-based data-driven models: gene expression programming (GEP), neural networks (NN), random forest (RF), multivariate adaptive regression spline (MARS), and support vector machine (SVM) in modeling soil phosphorus (P). In order to achieve this goal, several parameters, including soil pH, organic carbon (OC), clay content, and soil P data, were collected from different regions of the Neyshabur plain, Khorasan-e-Razavi Province (Northeast Iran). First, a discrete wavelet transform (DWT) was applied to the pH, OC, and clay as the inputs and their subcomponents were utilized in the applied data-driven techniques. Statistical Gamma test was also used for identifying which effective soil parameter is able to influence soil P. The applied methods were assessed through 10-fold cross-validation scenarios. Our results demonstrated that the wavelet&ndash, GEP (WGEP) model outperformed the other models with respect to various validations, such as correlation coefficient (R), scatter index (SI), and Nash&ndash, Sutcliffe coefficient (NS) criteria. The GEP model improved the accuracy of the MARS, RF, SVM, and NN models with respect to SI-NS (By comparing the SI values of the GEP model with other models namely MARS, RF, SVM, and NN, the outputs of GEP showed more accuracy by 35%, 30%, 40%, 50%, respectively. Similarly, the results of the GEP outperformed the other models by 3.1%, 2.3%, 4.3%, and 7.6%, comparing their NS values.) by 35%-3.1%, 30%-2.3%, 40%-4.3%, and 50%-7.6%, respectively.

The objectives of this study were to measure some trace element concentrations in the groundwater of the Khoy area in northwestern Iran, understand their potential origins using multivariate statistical approaches (correlation analysis, cluster analysis and factor analysis), and evaluate their non-carcinogenic human health risks to local residents through drinking water intake. The trace element status of the groundwater and the associated health risks in the study area have not previously been reported. Groundwater water samples were collected from 54 water sources in July 2017 in the study area. Samples were measured for EC, pH, major and minor elements and some trace elements (Fe, Mn, Al, Zn, Cr, Pb, Cd, Co, Ni and As). The levels of EC, F, Cd, Pb, Zn, As and all the major ions except K exceeded permissible levels for drinking water. Multivariate analysis showed that the quality of groundwater was mainly controlled by geogenic factors followed by anthropogenic impacts. Health risk assessment results indicated that Cr and As in the groundwater, with hazard quotient values of 0.0001 and 11.55, respectively, had the lowest and highest impacts of non-carcinogenic risk to adults and children in the area. The high-risk samples were mainly situated in the northeast and southwest of the Khoy plain where the groundwater was saline. The health risk associated with water consumption from the unconfined aquifer was higher than that from the confined aquifer in the study area. Special attention should be paid to groundwater management in the high-risk areas to control factors (e.g., EC, pH and redox) that stimulate the release of trace elements into groundwater.

Modelling reference evapotranspiration (ET0) is a key issue in hydrology, water resources management and irrigation scheduling. This article aimed at evaluating gene expression programming (GEP) and support vector machine (SVM) techniques for modelling ET0 in humid regions of South Korea. The GEP and SVM results were also compared with the empirical ET0 models with the same input parameters. Daily meteorological data from eight weather stations comprising a period of 10 years were utilized to establish the models. This study evaluated two different data management scenarios of simulations. At the first scenario, the heuristic and empirical models were developed and tested at each location, separately, which confirmed the superiority of heuristic models to empirical models. At the second scenario, two cross-station approaches were developed: modelling ET0 using data from ancillary stations and developing generalized heuristic models for the studied stations. The obtained results revealed that both the GEP and SVM techniques could simulate ET0 in both the scenarios, successfully. Meanwhile, GEP outperformed SVM in cross-station scenario.

An algorithm was proposed to classify wheat fields into high-productive and low-productive classes using satellite images according to a threshold value for rain-fed wheat ET a in the anthesis stage. Since the classification process was based on the ET a values in the rain-fed wheat pixels, an algorithm was proposed to map wheat fields using the combination of MODIS and Landsat-8 images. The wheat area mapping method included two major processes in which the first one used a step by step elimination process of non-wheat pixels which did not follow the standard wheat vegetation index time series; the second one used supervised classification methods for detecting the rain-fed wheat pixels. The assessment of the wheat area map was performed statistically using surveyed wheat plots. After detecting the wheat parcels, the threshold value was determined using a frequency analysis on actual evapotranspiration values in the parcels. The rain-fed wheat ET a values estimated from the SEBAL algorithm and were compared to the results from the Eagleman-Affholder method and MOD16A2 products. To assess if the wheat fields in productivity classes have been categorized correctly, yield values in the two classes were compared with each other. The rain-fed wheat yield was estimated using the light use efficiency model and compared to provincial census data for accuracy assessment. Results showed that the overall accuracy, Kappa coefficient, and F1 score values for the 250-m resolution map from the MODIS images were 82, 0.61, and 0.71, respectively. In the same order, these statistics for the 30-m resolution map from the Landsat-8 images were 92, 0.62, and 0.77, respectively. Both the SEBAL and the Eagleman-Affholder methods closely estimated the average wheat ET a value in the anthesis stage equal to 2.4 mm/day. The mean of the rain-fed wheat yield value from the LUE model in 2015 was 10% lower than the census data. Relying on the low amount of the absolute error between the LUE model and the census data, the mean of the yield values in both high-productive and low-productive classes were compared with together. Results showed that the mean amount of yield in high-productive class was 33% (271 kg/ha) more than that of in low-productive class.

Rapid population growth, industrialization, and agricultural expansion in the Khoy area (northwestern Iran) have led to its dependence on groundwater and degradation of groundwater quality. This study attempts to decipher the major processes and factors that degrade the groundwater quality of the Khoy plain. For this purpose, 54 groundwater samples from unconfined and confined aquifers of the plain were collected in July 2017 and analyzed for major cations and anions (Na, K, Ca, Mg, HCO3, SO4, and Cl), minor ions (NO3 and F), and Al. Magnesium and bicarbonate were identified as the dominant cation and anion, respectively. Several ionic ratios and geochemical modeling using PHREEQC indicated that the most important hydrogeochemical processes to affect groundwater quality in the plain were weathering and dissolution of evaporitic and silicate minerals, mixing, and ion exchange. There were smaller effects from evaporation and anthropogenic factors (e.g., industries). Results showed that the high salinity of the groundwater in the northeast area of the plain was due to the high solubility of the evaporitic minerals, e.g., halite and gypsum. Reverse ion exchange and the contribution of mineral dissolution were more significant than ion exchange in the northeastern part of the plain. Elevated salinity of the groundwater in the southeast was attributed mostly to reverse ion exchange and somewhat to evaporation.

Due to having a short half-life and novel site of action, the herbicidal potential of natural compounds are lionized. Coumarin is a secondary metabolite from Lavandula sp., family Lamiacae. The impact of eight concentrations of coumarin (0, 100, 200, 400, 800, 1600, 3200 and 6400 ppm) were separately used as a pre-plant incorporated into soil on six plant species under greenhouse conditions. Generally, coumarin had phytotoxic effect against all plant species. The phytotoxic effect was concentration-dependent. The high concentrations could inhibit the emergence of seedlings (probably by stopping germination of seeds). Based on ED50 parameter, the ranking of plant species for tolerance to coumarin was S. halepense > Z. mays > C. album > A. retroflexus > E. cruss-gali > P. oleracea. Based on selectivity index, coumarin at a concentration of 365.69 ppm can control P. oleracea without damaging Z. mays, whereas any concentration it cannot control other weeds without damaging Z. mays.

Artificial intelligence and empirical ET0 models are evaluated.Local and regional scenarios were developed and tested.GEP outperforms the other applied models in ET0 estimation.Arid regions offer the lowest accuracies among the studied stations. Accurate estimation of reference evapotranspiration (ET0) values is of crucial importance in hydrology, agriculture and agro-meteorology issues. The present study reports a comprehensive comparison of empirical and semi empirical ET0 equations with the corresponding Heuristic Data Driven (HDD) models in a wide range of weather stations in Iran. Artificial Neural Networks (ANNs), Adaptive Neuro-Fuzzy Inference System (ANFIS), Support Vector Machine (SVM) and Gene Expression Programming (GEP) techniques are applied for modeling ET0 values considering different data management scenarios, and compared with corresponding Hargreaves-Samani (HS), Makkink (MK), Priestley-Taylor (PT), and Turc (T) ET0 models as well as their linear and non-linear calibrated versions along with the regression-based Copais algorithm. The obtained results confirm the superiority of GEP-based models. Further, the HDD models generally outperform the applied empirical models. Among the empirical models, the calibrated HS model found to give the most accurate results in all local and pooled scenarios, followed by the Copais and the calibrated PT models. In both local and pooled applications, the calibrated HS equation should be applied when no training data are available for the use of HDD models. The best results of the models correspond to the humid regions, while the arid regions provide the poorest estimates. This may be attributed to higher ET0 values associated with these stations and the high advective component of these locations.

A comprehensive analytical solution is presented for the steady‐state two‐dimensional drainage problem of the water flow from a horizontal ponded surface into rectangular cross‐section array ditches. The ditches partially penetrate into a homogeneous and isotropic porous medium of finite depth over an impervious layer. The solution includes the equations for drainage discharge into ditches, the drainage discharge through the seepage face and the water depth portion of a ditch, and seepage velocities along the ponded surface. The equations are obtained using the Schwarz–Christoffel transformation in the conformal mappings of the physical and hodograph planes on to an auxiliary plane. The proposed analytical solution is validated by comparing the results with the known experimental data and the other analytical works. The results of the analyses show that seepage velocity along the ponded surface water decreases with distance from the ditch and accordingly leaching of salts is non‐uniform. Closer spacing between two adjacent ditches, shallower water depth in the ditches, and deeper depth of the ditches are advantageous in removing salts from fields.

Droplet characteristics and precipitation profiles of a fixed spray plate sprinkler (FSPS) were characterised in some indoor experiments were conducted using various deflection plates (36-grooved blue, 30-grooved green and 36-grooved black plates). Four nozzle diameters (2.78, 3.97, 4.76 and 7.14 mm) were mounted on the sprinkler and operated at three pressures (69, 138 and 241 kPa) and at a nozzle height of 1.5 m. Drop characteristics were determined using a low speed digital photography method. The results showed that by increasing the nozzle size at a fixed operating pressure the resulted wetted diameter, peak application rate, droplet sizes and velocities were increased. With smaller nozzle diameters (2.78 and 3.97 mm) drop diameter increased as working pressure increased, while with larger diameters (4.76 and 7.14 mm) a reverse trend between drop size and working pressure was observed. Empirical equations were developed to estimate the wetted diameter and also volumetric mean diameter (VMD) and volume median diameters (D50) of droplets at different distances from the sprinkler as functions of sprinkler configurations.

Summary When dealing with climatic variables, the performance assessment of many Artificial Intelligence (AI) and/or data mining applications is based on a single data set assignment of the training and test sets. Further, it is very usual that this assignment is defined according to a local and temporary criterion, i.e. the models are trained and tested using data of the same station. Based on this procedure, the performance of the models outside the training location cannot be inferred. The present work evaluates the performance of Gene Expression Programming (GEP) based models for estimating reference evapotranspiration (ET0) according to temporal and spatial criteria and data set scanning procedures in coastal environments of Iran. The accuracy differences between the local and the external performance depend on the specific climatic trends of the test stations, as well as on the input combination used to feed the models. When relying on a suitable input selection, externally trained models might be a valid alternative to locally trained ones, which would be a crucial advantage in places where only limited climatic variables are available. K-fold testing is a good choice to prevent partially valid conclusions derived from model assessments based on a simple data set assignment. Further, calibration of the GEP model may not be needed, if enough climatic data are available at other stations for external model application. The performance of the GEP model fluctuates chronologically and spatially. A suitable assessment of the model should consider a complete local and/or external scan of the data set used.

The improvement of methods for estimating reference evapotranspiration (ET0) requiring few climatic inputs is crucial, due to the partial or total lack of climatic inputs in many situations. The current paper compares the effect of local and external training procedures in neuro-fuzzy and neural network models for estimating ET0 relying on two input combinations considering k-fold testing. Therefore, different data set configurations were defined based on temporal and spatial criteria allowing for a complete and suitable testing scan of the complete data set. The proposed methodology enabled the comparison in each station of models trained with local data series and models trained with the data series from the remaining stations. Results showed that the external training based on a suitable input choice and a representative pattern collection might be a valid alternative to the more common local training.

Temperature and solar radiation-based modeling procedures are reported in this study for estimating daily reference evapotranspiration (ET0) by using gene expression programming (GEP) and adaptive neuro-fuzzy inference system (ANFIS). A comparison is also made among these techniques and the corresponding traditional temperature/radiation-based ET0 estimation equations. Two data management scenarios were evaluated for estimating ET0: (1) the models were trained and tested using the local data of each studied weather station; and (2) the models were trained using the pooled data from all the stations and tested in each individual station. The GEP and ANFIS models were found to be better than the Hargreaves–Samani, Makkink and Turc ET0 equations in the first scenario. Comparison of GEP and ANFIS models trained with pooled data and tested for each station showed that the ANFIS models generally performed better than the GEP models. However, the comparison of GEP and ANFIS models trained and tested with pooled data revealed that the GEP models performed better than the ANFIS models in the second scenario.

In this study, the effects of different parameters on flow structure and flow hydraulics for combining sewer junction consisting of manhole and lateral inflow were investigated numerically. For this purpose, the effects of dimension variations, main and lateral pipe slope, angle and the joint position of lateral pipe beside manhole, manhole geometry, and the outflow rate of manhole on flow structure and energy loss coefficients were investigated. Results show that the rate of energy loss decreases by heightening the installation position of lateral pipe beside manhole. In addition, it was shown that the slope variations of main and lateral pipes and outflow rate do not affect the rate of energy loss.

The problem of water movement through the root zone has attracted increasing interest during the last few decades. In this research, the spatial and temporal pattern of root water uptake in wetted soil was studied in the root zone of a 6-year-old apple tree. An important part of the root water uptake model is root length density, which was measured by sampling soil cores in one quarter of the root zone. The exponential model better described the observed apple root distribution. The measured data were compared against the outputs of the root density distribution model. A normalized root length density was used to simulate root water uptake and determine the effect of root distribution on the water content pattern. A 2-dimensional (2D) model of root water uptake was established, which includes root density distribution function, potential transpiration, and the soil water stress-modified factor. Root water uptake distribution was measured with an array of time domain reflectometry (TDR) probes. Root water uptake parameters were optimized by minimizing the residuals between measured and simulated soil water content values. Studies showed that the maximum root water uptake was at a depth of 10-50 cm. The results indicated an excellent agreement between the measured and the simulated values, proving that the developed root water uptake model is effective and feasible.

Drip irrigation is considered as one of the most efficient irrigation systems. Knowledge of the soil wetted perimeter arising from infiltration of water from drippers is important in the design and management of efficient irrigation systems. To this aim, numerical models can represent a powerful tool to analyze the evolution of the wetting pattern during irrigation, in order to explore drip irrigation management strategies, to set up the duration of irrigation, and finally to optimize water use efficiency. This paper examines the potential of genetic programming (GP) in simulating wetting patterns of drip irrigation. First by considering 12 different soil textures of USDA–SCS soil texture triangle, different emitter discharge and duration of irrigation, soil wetting patterns have been simulated by using HYDRUS 2D software. Then using the calculated values of depth and radius of wetting pattern as target outputs, two different GP models have been considered. Finally, the capability of GP for simulating wetting patterns was analyzed using some values of data set that were not used in training. Results showed that the GP method had good agreement with results of HYDRUS 2D software in the case of considering full set of operators with R2 of 0.99 and 0.99 and root mean squared error of 2.88 and 4.94 in estimation of radius and depth of wetting patterns, respectively. Also, field experimental results in a sandy loam soil with emitter discharge of 4 L h-1 showed reasonable agreement with GP results. As a conclusion, the results of the study demonstrate the usefulness of the GP method for estimating wetting patterns of drip irrigation. El riego por goteo está considerado como uno de los sistemas de riego más eficientes. El conocimiento del perímetro del bulbo mojado durante la fase de infiltración del agua es importante para el proyecto y manejo de sistemas de riego por goteo eficientes. Los modelos numéricos son una herramienta útil para analizar la evolución del bulbo mojado durante el riego a fin de explorar estrategias de manejo del riego por goteo que determinen el tiempo de riego y optimicen la eficiencia del uso del agua. En este trabajo se examinó el potencial de la programación de algoritmos genéticos (GP) para la simulación de la forma de bulbos mojados en riego por goteo. En primer lugar se ha simulado, con el programa de métodos numéricos HYDRUS 2D, el bulbo mojado en 12 texturas de suelo y diferentes caudales de goteros y tiempos de riego. A partir de las estimaciones de la profundidad y radio mojado como variables objetivo, se han considerado dos modelos GP diferentes. Por último, se ha analizado la capacidad de GP para simular la forma del bulbo mojado a partir de valores que no se utilizaron durante el proceso de entrenamiento. Los resultados obtenidos con GP, considerando el conjunto completo de operadores, se ajustaron, razonablemente, a los estimados con HYDRUS 2D, obteniéndose en la estimación del radio y la profundidad del bulbo mojado, coeficientes R2= 0,99 en ambos casos y valores de error cuadrático medio de 2,88 y 4,94 respectivamente. Los resultados experimentales de campo en un suelo franco arenoso con caudal del emisor de 4 L h-1 concordaron razonablemente con los de GP. Los resultados del estudio demuestran la utilidad de este método para estimar la forma del bulbo mojado en riego por goteo.

Summary Evapotranspiration, as a major component of the hydrological cycle, is of importance for water resources management and development, as well as for estimating the water budget of irrigation schemes. This study presents a Gene Expression Programming (GEP) approach, for estimating daily reference evapotranspiration (ET0) in four weather stations in Basque Country (Northern Spain), for a 5-year period (1999–2003). The data set comprising air temperature, relative humidity, wind speed and solar radiation was employed for modeling ET0 using FAO-56 Penman Monteith equation as the reference. The GEP results were compared with the Adaptive Neuro-Fuzzy Inference System (ANFIS), Priestley–Taylor and Hargreaves–Samani models. Based on the comparisons, the GEP was found to perform better than the ANFIS, Priestley–Taylor and Hargreaves–Samani models. The ANFIS model is ranked as the second best model.

32 Pag., 7 Fig., 2 Tabl. The definitive version is available at: http://www.sciencedirect.com/science/journal/15375110, The application pattern for single, stationary spray sprinklers exposed to the wind was modelled to simulate water application in a centre pivot-irrigated field. In the model, a dynamic square grid of cells for water application and a static square grid of cells for water collection were defined. The dynamic grid contained the information on the water application pattern of an isolated spray sprinkler, and this followed the motion of the centre pivot lateral. The static grid represented the entire field, and received water from the dynamic grid at fixed time intervals. Model outputs included the applied water distribution pattern and measures of irrigation uniformity (radial, travelling path and global). A series of experiments using pivoted and single spray sprinklers were conducted simultaneously. The results from the model compared well with field observations. The resulting root mean square errors for the Heermann and Hein uniformity coefficient and the average applied water depth were 0.02% and 0.08 mm, respectively. Model simulations were carried out to illustrate the effect of wind on irrigation uniformity., R. Delirhasannia received a scholarship from the Ministry of Science, Research and Technology of Iran to do research at the Aula Dei Experimental Station of CSIC, Zaragoza, Spain.

The relationship between capillary pressure and saturation has a remarkable value in investigating interactions of two immiscible fluids in porous media. Pore network models, as predictors of fluids movement in porous media, can play undeniable role in determining the mentioned relationship. In the current study, the results of numerical pore network modelling, which represents pore structure of a real porous medium with networks of pore bodies connected with pore throats, are used for computing the macroscopic relationship between capillary pressure and saturation. A notable volume of soil is influenced by water source and according to the results of previous studies, it seems practically impossible to simulate water movement in macro scale dimensions by use of pore scale models. Meanwhile a suitable solution was found in this study, by considering a thin vertical soil column under the water source which was divided to smaller volumes as cells, by horizontal crossings. Each cell was considered as a pore network unit, so the soil column was consisted of series of pore network units which were vertically jointed to each other. The moisture distribution and also wetting front movement in the column were determined by application of pore network model, using the dynamic update saturation solution method. The solution was conducted between each pair of consequent cells in an alternative manner within a time step since arrival time of the water from an upper cell to the lower one. Moreover, for evaluation of the model ability, soil moisture profiles in a sandy soil of an experimental tank under the water source were studied. Comparison of the simulation and observation data confirmed the high ability of the column pore network model in prediction of the moisture distribution and wetting front movement in a soil column.